Hybrid approach for alignment of a pre-processed three-dimensional point cloud, video, and CAD model using partial point cloud in retrofitting applications

Acquiring the three-dimensional point cloud data of a scene using a laser scanner and the alignment of the point cloud data within a real-time video environment view of a camera is a very new concept and is an efficient method for constructing, monitoring, and retrofitting complex engineering models in heavy industrial plants. This article presents a novel prototype framework for virtual retrofitting applications. The workflow includes an efficient 4-in-1 alignment, beginning with the coordination of pre-processed three-dimensional point cloud data using a partial point cloud from LiDAR and alignment of the pre-processed point cloud within the video scene using a frame-by-frame registering method. Finally, the proposed approach can be utilized in pre-retrofitting applications by pre-generated three-dimensional computer-aided design models virtually retrofitted with the help of a synchronized point cloud, and a video scene is efficiently visualized using a wearable virtual reality device. The prototype method is demonstrated in a real-world setting, using the partial point cloud from LiDAR, pre-processed point cloud data, and video from a two-dimensional camera.

[1]  Ashok Kumar Patil,et al.  A LiDAR and IMU Integrated Indoor Navigation System for UAVs and Its Application in Real-Time Pipeline Classification , 2017, Sensors.

[2]  Sanghun Nam,et al.  SPACESKETCH: Shape modeling with 3D meshes and control curves in stereoscopic environments , 2012, Comput. Graph..

[3]  Burcu Akinci,et al.  A formalism for utilization of sensor systems and integrated project models for active construction quality control , 2006 .

[4]  Petr Novák,et al.  The Design of 3D Laser Range Finder for Robot Navigation and Mapping in Industrial Environment with Point Clouds Preprocessing , 2016, MESAS.

[5]  Albert S. Huang,et al.  Visual Odometry and Mapping for Autonomous Flight Using an RGB-D Camera , 2011, ISRR.

[6]  Kevin Tansey,et al.  The potential for using 3D visualization for data exploration, error correction and analysis of LiDAR point clouds , 2012 .

[7]  Dieter Fox,et al.  Interactive 3D modeling of indoor environments with a consumer depth camera , 2011, UbiComp '11.

[8]  P. R. McAree,et al.  How do ICP variants perform when used for scan matching terrain point clouds? , 2017, Robotics Auton. Syst..

[9]  Francisco Morán,et al.  Automatic video to point cloud registration in a structure-from-motion framework , 2015, 2015 IEEE International Conference on Image Processing (ICIP).

[10]  Wolfram Burgard,et al.  An evaluation of the RGB-D SLAM system , 2012, 2012 IEEE International Conference on Robotics and Automation.

[11]  Carol C. Menassa,et al.  Virtual Retrofit Model for aging commercial buildings in a smart grid environment , 2014 .

[12]  A. Patil,et al.  An adaptive approach for the reconstruction and modeling of as-built 3D pipelines from point clouds , 2017 .

[13]  Kok-Lim Low Linear Least-Squares Optimization for Point-to-Plane ICP Surface Registration , 2004 .

[14]  Krystof Litomisky Consumer RGB-D Cameras and their Applications , 2012 .

[15]  Paul J. Besl,et al.  A Method for Registration of 3-D Shapes , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[16]  Majd Alshawa lCL: Iterative closest line A novel point cloud registration algorithm based on linear features , 2007 .

[17]  Gérard G. Medioni,et al.  Object modelling by registration of multiple range images , 1992, Image Vis. Comput..

[18]  Holly E. Rushmeier,et al.  The 3D Model Acquisition Pipeline , 2002, Comput. Graph. Forum.

[19]  Gerd Bruder,et al.  Poster: Immersive point cloud virtual environments , 2014, 2014 IEEE Symposium on 3D User Interfaces (3DUI).

[20]  Sven Behnke,et al.  Registration with the Point Cloud Library: A Modular Framework for Aligning in 3-D , 2015, IEEE Robotics & Automation Magazine.

[21]  Ji Zhang,et al.  LOAM: Lidar Odometry and Mapping in Real-time , 2014, Robotics: Science and Systems.

[22]  Nico Blodow,et al.  Aligning point cloud views using persistent feature histograms , 2008, 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[23]  Nico Blodow,et al.  Towards 3D Point cloud based object maps for household environments , 2008, Robotics Auton. Syst..

[24]  R. Berkvens,et al.  A Benchmark Survey of Rigid 3D Point Cloud Registration Algorithms , 2015 .

[25]  Jan Boehm,et al.  Automatic Geometry Generation from Point Clouds for BIM , 2015, Remote. Sens..

[26]  Radu Bogdan Rusu,et al.  Semantic 3D Object Maps for Everyday Manipulation in Human Living Environments , 2010, KI - Künstliche Intelligenz.